Image enhancement is a subfield in digital image processing which aims to improve the interpretability or perception of information in images for human viewers, or to provide better input for other automated image processing techniques.
Generally speaking, image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods. While frequency domain methods are carried out on the frequency domain of an image, spatial domain methods operate directly on pixels of an image. Among all the methods, histogram equalization and histogram specification both belong to the category of spatial domain techniques and offer effective approaches in image enhancement.
To adjust the contrast of an image, histogram equalization is often implemented. Roughly speaking, an image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. The histogram plots the number of pixels in the image (vertical axis) with a particular brightness value (horizontal axis). Histogram equalization spreads out the most frequent intensity values. After equalization, the gray level in object image is set to be uniformly distributed.
Different from histogram equalization which makes the gray level in the object image uniform, histogram specification (or histogram matching) offers another flexible and efficient way of image enhancement. The process of histogram specification takes in an input image and produces an output image that is based upon a particular histogram that can be pre-defined. The histogram specification can be degraded into histogram equalization if the gray level in the object image is set to be uniformly distributed.
Since the process of histogram specification depends on the histogram that is specifically defined, it is essential for histogram specification to choose a suitable shape of the histogram, i.e., to choose a density function of the histogram. Several methods of histogram specification so far have been used to improve the performance of histogram specification. For example, some proposals are formulated utilizing a single Gaussian function having the average grey level value of the input image as the expectation and the contrast value as the standard deviation to specify a histogram. Unfortunately, those proposals utilizing a single Gaussian function suffer from loss of image details.
It would therefore be desirable to have systems and methods that implement a histogram specification to improve the reproduction of image detail while enhancing the quality of an image effectively.